The elevation of atmospheric CO₂ concentration leads to a decline in the mineral composition of C3 plants (Gojon et al., 2023). The negative effect of elevated CO₂ on plant mineral composition has been observed worldwide, and alters the content of nutrients that are essential for human nutrition, such as nitrogen (N) and proteins, iron (Fe) or zinc (Zn) (Loladze, 2014). Therefore, the rise in atmospheric CO₂ poses a significant threat to food security in the coming decades. Indeed, several modeling approaches predict a decrease in plant-based nutrient availability due to the negative effect of elevated CO₂ on the mineral status of plants, leading to an additional risk of nutritional deficiency for hundreds of millions of people (Medek et al., 2017; Ebi and Loladze, 2019). However, the reasons why elevated CO₂ leads to the degradation of plant mineral composition are far from being well understood. To date, only a few genes with a potential regulatory role on this effect have been identified (Gao et al., 2019; Umnajkitikorn et al., 2020; Yang et al., 2020; Bouain et al., 2022; Sun et al., 2022; Cassan et al., 2023). Despite their limited number, these studies show that the detrimental effect of high CO₂ on the plant mineral status has genetic bases. In addition to this, several reports suggest that exploring the natural genetic variability of plants represents a major opportunity to understand the mechanisms by which high CO₂ leads to a decline in plant mineral composition (Myers et al., 2014; Zhu et al., 2018; Marcos-Barbero et al., 2021). Indeed, a significant diversity in the response of mineral composition to high CO₂ has been observed in several plant species. For protein and therefore N content, as well as for Fe or Zn content, substantial variations have been observed between small panels of genotypes from different species (Myers et al., 2014; Zhu et al., 2018; Marcos-Barbero et al., 2021). This implies the presence of a genetic diversity reservoir, which can facilitate the understanding of the ionome’s response to high CO₂ and subsequently provide an opportunity to alleviate this negative impact. However, in order to identify the genetic determinants of this negative response of the ionome to high CO₂, large-scale approaches are necessary, but are still lacking for the moment. The objective of this work was to fill the aforementioned knowledge gap by using a large collection of natural genotypes of the model plant Arabidopsis thaliana. This allowed to explore in depth the natural variation of the ionome response to elevated CO₂, and to generate a resource of phenotypic data that can be used in association genetics approaches. To this end, we used several hundreds of accessions from different geographic scales of A. thaliana, and analyzed their leaf mineral composition under contrasted conditions of CO₂ concentration. This allowed us to extract the general trends in the leaf ionome response to high CO₂, and to identify a large set of genes associated with the variation in the mineral composition of plants in response to high CO₂. By combining this information with genome expression data under elevated CO₂, we end up by functionally validating one of these genes for its importance in the reduction of Zn content under elevated CO₂. Our results open the way for a better understanding of the genetic and molecular mechanisms involved in the regulation of plant mineral nutrition by the elevation of atmospheric CO₂.

In order to explore the natural variation and identify its underlying genetic basis associated with the negative effect of elevated CO₂ on plant ionome, we used three populations of A. thaliana representing different geographic scales (i.e. the worldwide REGMAP population, the LANGUEDOC regional population and the local TOU-A population from east of France) and displaying different levels of genetic diversity (Figure 1A). These populations were grown under ambient or elevated CO₂, and we measured in each accession the composition of their ionome in rosettes, including C, N, Na, Fe, Mg, Mn, Zn, and Cu content.

Figure 1

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The ionome of Arabidopsis thaliana natural accessions displays a high range of phenotypic diversity in response to elevated CO₂

To explore the effect of elevated CO₂ in each accession, we calculated the relative change in nutrient composition of A. thaliana accessions from the three populations in response to elevated CO₂. In agreement with the results previously mentioned, we observed that the median relative change of most nutrient content at the population-level was negatively affected by elevated CO₂ (Figure 2). But the most striking observation was the genetic diversity of ionome response observed in these populations. Indeed, while most the natural accessions were negatively affected by elevated CO₂ (with a negative relative ratio of their nutrient content between ambient and elevated CO₂), a considerable number of accessions were rather not affected by elevated CO₂, or even positively affected, therefore showing an improved nutrient composition under elevated CO₂. For macronutrients like N, the relative change of concentration between ambient and elevated CO₂ varied from 20% to –50%, and for micronutrients like Cu, Fe or Zn, the relative change of concentration between ambient and elevated CO₂ varied from 100% to –60% (Figure 2). In addition, some differences among nutrients were observed between populations. For instance, a smaller dispersion of Fe relative change in the LANGUEDOC population, against a higher distribution of Mn relative change.

Figure 2

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In order to explore the behavior of the different elements in response to elevated CO₂ and to observe the structure of phenotypic variation, we performed a principal component analysis (PCA) of the relative change in the 8 elements for the accessions from the three populations. The accessions from all populations seem to have globally similar responses to elevated CO₂, as suggested by the overlap of the three populations in the two first principal components (Figure 3A). The first component of the PCA described a clear antagonistic trend between C content and the change of other mineral elements (Figure 3B), suggesting that most of the variation between accessions in term of mineral response (almost 40%) could be driven by one or a few mechanisms resulting in an inverse variation between the whole ionome and C change (Figure 3B). Interestingly, the second component, explaining almost 15% of the variation among accessions in term of mineral response, was mainly driven jointly by change in N and C concentration. Altogether, these results show that there is a marked and large variability among accessions in their mineral concentration in response to elevated CO₂, illustrated by accessions negatively affected by elevated CO₂ and others positively affected by elevated CO₂.

Figure 3

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Genetic architecture of the ionome response to elevated CO₂, and identification of genetic determinants

In order to explore specific behavior of sub-populations, we clustered the accessions from the REGMAP panel via a k-means approach (Figure 4). This multivariate clustering resulted in the partitioning of accessions in three groups (Supplementary file 1A). Cluster 1 displayed the most negative pattern of ionome response to elevated CO₂. Inversely, accessions included in Cluster 2 displayed a globally positive response, with the highest relative change for almost all mineral elements, except for C content. These accessions did not appear to be clustered geographically with respect to their collection origin in the REGMAP panel (Figure 4—figure supplement 1), which is in line with the high genetic diversity of response to elevated CO₂ observed at smaller geographical scales (Figure 2). Finally, Cluster 3 displayed a resilient pattern, with accessions showing a globally attenuated response to elevated CO₂. Interestingly, the large phenotypic diversity of the ionome observed in the three populations in response to high CO₂, as well as the presence of contrasted subpopulations in the REGMAP panel, suggests the presence of genetic determinants associated with this response.

Figure 4 with 1 supplement see all

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We ran Genome-Wide Association (GWA) mapping to describe the genetic architecture of the ionome response to elevated CO₂, and to fine-map candidate genes underlying the detected quantitative trait loci (QTLs). We focused here on the phenotypic data collected on the REGMAP population, and used the sequencing data available for this population (Arouisse et al., 2020). We included in this analysis the level of each mineral under ambient and under elevated CO₂, as well as the relative change between ambient and elevated CO₂ for each element. We also included a trait corresponding for each accession to the coordinate on the first and on the second PCA axes (PCA1 and PCA2) explaining collectively more than 50% of ionomic variation (Figure 3). Therefore, these values correspond to traits driving and summarizing a large part of the ionome variation under elevated CO₂. This resulted as a whole in running GWA mapping on 30 different single-trait GWAS. The overall approach was first validated by observing expected results for traits phenotyped under ambient CO₂. For instance, we observed a very strong peak for the Na content at the locus of the HKT1 gene (Figure 5—figure supplement 1A), which is known to be involved in the natural genetic variation of Na content in Arabidopsis thaliana (Baxter et al., 2010), or a strong peak for the N content at the locus of the NIA1 gene (Figure 5—figure supplement 1B), encoding for an isoform of the nitrate reductase required for the first step of nitrate reduction and associated with natural genetic variation of N content in A. thaliana (North et al., 2009).

GWA mapping revealed a polygenic architecture for each phenotypic trait, although its complexity largely differs among traits (Figure 5). For instance, very few and neat peaks of association were detected Na and Mn content under elevated CO2, or of Fe and Cu relative change between ambient and elevated CO₂ (Figure 5—figure supplements 2 and 3). On the other hand, a more complex genetic architecture with the detection of a large number of QTLs was observed for traits related to N or C content (Figure 5—figure supplements 2 and 3). For each of the traits that have been analyzed under elevated CO₂ or corresponding to the relative change of their content between ambient and elevated CO₂, we isolated the 50 SNPs with the most significant p-value, hereafter named top SNPs (Figure 5A and Supplementary file 1B and C). In order to identify the overlap between the genetic architecture of each trait, we looked whether some of the top SNPs were shared among traits. While the large majority of SNPs were specific to one trait, 30 and 21 SNPs were shared between two traits for the content under elevated CO₂ or for the relative change between ambient and elevated CO₂, respectively (Figure 5B and Supplementary file 1B and C). In addition, 8 and 2 SNPs were shared between three traits for the content under elevated CO₂ or for the relative change between ambient and elevated CO₂, respectively (Figure 5B and Supplementary file 1B and C). Most of the shared SNPs were associated with micronutrients (Fe, Mn, Zn, and Mg content) and with N and/or with the first component of the PCA axis. An interesting QTL located on chromosome 1 was notably associated with 6 traits, displaying SNPs shared between Mn, Zn, and N relative change and SNPs shared between Mn, N, and PC1 content under elevated CO₂ (Figure 5A and Supplementary file 1B and C). Another QTL located on chromosome 3 encompasses SNPs shared between Fe, Zn, and PC1 content under elevated CO₂ (Figure 5A and Supplementary file 1B and C).

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We next identified for each trait a list of the genes located at ±25 kb from the top 50 SNPs, which corresponds to the rough estimate of the decay of linkage disequilibrium identified in A. thaliana at the worldwide scale (Kim et al., 2007). This resulted in a list of genes for each element, ranging from 154 to 422 genes depending on the element (Supplementary file 1B and C). Among others, several genes associated with top 50 SNPs were identified as obvious candidates of the effect of elevated CO₂ on plant nutrition and ionome content. This was the case of ZINC INDUCED FACILITATOR 1 (ZIF1, AT5G13740) and ZIF-LIKE1 (AT5G13750), linked with SNPs identified for Zn content under elevated CO₂, and involved Zn sequestration mechanisms (Lee et al., 2021). We also noticed the link between SNPs identified for Zn relative change and TIP2;2 (AT4G17340), known to be involved in Zn root-to-shoot translocation (Wang et al., 2022). Concerning N relative change, some of the top 50 SNPs were linked to ASN1 (AT3G47340), which is an actor of N status and remobilization (Lam et al., 2003; Gaufichon et al., 2017). Some of the top 50 SNPs identified for Fe relative change were linked to MCO2 (AT5G21100) and MCO3 (AT5G21105) genes, which have been recently characterized as actors of the regulation of Fe homeostasis (Brun et al., 2022). Finally, it is interesting to note that the QTL located on chromosome 3 mentioned above displaying significant shared SNPs identified for Fe, Zn, and PC1 content under elevated CO₂ was associated among other genes with ISU2 (AT3G01020), coding for one of the Fe-S clusters in Arabidopsis thaliana, which are known to be essential for photosynthesis and metabolism (Balk and Schaedler, 2014). Altogether, this demonstrated that genes identified through this approach represent a large and valuable reservoir of candidates to study and to counteract the effect of elevated CO₂ on plant nutrition and ionome content.

To analyze how these genes identified by GWA mapping are regulated by elevated CO₂, we performed RNA-seq from shoots and roots grown under ambient and elevated CO₂. Differentially expressed genes (DEG) associated to the effect of elevated CO₂ were identified from shoots and roots (Supplementary file 1D). We compared the list of shoots or roots elevated CO₂-DEG with the list of genes identified by GWA mapping for each element, which resulted in a list of 182 genes identified by GWA mapping and differentially regulated by elevated CO₂ in shoot or in roots (Supplementary file 1E), making them relevant candidates to be involved in the response of the mineral composition of plants to elevated CO₂. Most of these genes were deregulated by elevated CO₂ in shoot (Figure 6A and B). In shoot or in roots, these genes mainly showed an association with C-, Mg-, or Zn-related traits (Figure 6A and B). Several of these genes, identified by GWA mapping and whose expression is deregulated in response to high CO₂, were known for their role in nutrient homeostasis. This was the case for the ASN1 and DUR3 genes, encoding an asparagine synthase and a urea transporter involved in N metabolism and remobilization, both associated here with a N-related peak of association, and whose expression is modulated by high CO₂ in leaves (Figure 6C). We also observed in the leaves an interesting profile for several genes related to C metabolism and photosynthesis. This was the case for the BGP3 gene, involved in chloroplast development, or for the carbonic anhydrase CA1, both showing a decreased expression in response to high CO₂ and both associated with a peak in C-related GWA mapping under elevated CO₂ (Figure 6C). In roots, the gene most deregulated in response to high CO₂ was AT1G64710, encoding a GroES-type alcohol dehydrogenase, which interestingly is also deregulated in leaves (Figure 6D). We also observed in the roots that the expression of the TIP2;2 gene, associated here with a peak detected for Zn relative change GWA mapping, was deregulated in response to elevated CO₂ (Figure 6D).

Figure 6

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To go further, we selected one of the association peaks identified by GWA mapping, and sought to functionally validate the importance of this QTL in response to elevated CO₂, in order to demonstrate the value of our data set and GWA mapping analyses. To do so, we selected an association peak located on chromosome 4 and associated with Zn relative change (Figure 7A). More precisely, this association peak displayed the SNPs with the most significant p-values and more largely three SNPs that fell into the top 10 SNPs of the trait corresponding to the Zn relative change between ambient and elevated CO₂. The SNPs corresponding to the alternative alleles were associated to an increase of Zn content under elevated CO₂ (Figure 7B). These SNPs are located very close to the TIP2;2 (AT4G17340) gene, which has been recently characterized as an actor of Zn root-to-shoot translocation (Wang et al., 2022). We thus selected a set of accessions from haplotype 0 (reduced Zn content under elevated CO₂) or haplotype 1 (increased Zn content under elevated CO₂), and analyzed TIP2;2 expression in the roots under ambient and elevated CO₂. This analysis revealed a haplotype-specific difference in TIP2;2 expression: under ambient and elevated CO₂, accessions from haplotype 1 (correlated with a higher Zn content in the shoot (Figure 7B)) show a reduced TIP2;2 expression in the roots compared to those from haplotype 1 (Figure 7C and D), with a reduction being more pronounced under elevated CO₂. To test the effect of TIP2;2 expression of Zn content under elevated CO₂, we used the tip2;2–1 knock-out mutant and compared its Zn content under ambient and elevated CO₂ to this of the WT. We observed that the tip2;2–1 mutant line did not present any decrease in Zn shoot content in response to elevated CO₂, in opposition to what is observed for the WT (Figure 7E). In the tip2;2–1 mutant, Zn content under elevated CO₂ was even slightly higher than under ambient CO₂. Altogether, these results demonstrated that these data sets generated in this study and the associated analyses are a valuable resource to identify genes and associated mechanisms involved in the effect of elevated CO₂ on the mineral composition of plants.

Figure 7

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Data and R notebooks containing the analyses performed in this article can be found at https://src.koda.cnrs.fr/groups/ipsim/sirene-team (copy archived at Cassan et al., 2024). RNA-seq data generated for this study are available at https://www.ebi.ac.uk/biostudies/arrayexpress/studies using the accession no E-MTAB-13661.

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Author details

1. Oceane Cassan

   IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

2. Lea-Lou Pimpare

   IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

   Contribution

   Methodology

   Competing interests

   No competing interests declared

3. Timothy Mozzanino

   IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

   Contribution

   Formal analysis, Validation, Methodology

   Competing interests

   No competing interests declared

4. Cecile Fizames

   IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

   Contribution

   Formal analysis, Visualization

   Competing interests

   No competing interests declared

5. Sebastien Devidal

   Montpellier European Ecotron, Univ Montpellier, CNRS, Campus Baillarguet, Montpellier, France

   Contribution

   Methodology

   Competing interests

   No competing interests declared

6. Fabrice Roux

   Laboratoire des Interactions Plantes-Microbes-Environnement, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS, Université de Toulouse, Castanet-Tolosan, France

   Contribution

   Resources, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

7. Alexandru Milcu

   1. Montpellier European Ecotron, Univ Montpellier, CNRS, Campus Baillarguet, Montpellier, France
   2. CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

   Contribution

   Conceptualization, Funding acquisition, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

8. Sophie Lebre

   IMAG, Univ Montpellier, CNRS, Montpellier, France

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology

   Competing interests

   No competing interests declared

9. Alain Gojon

   IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

   Contribution

   Conceptualization, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

10. Antoine Martin

    IPSiM, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    antoine.martin@cnrs.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6956-2904

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